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Utilizzabile 24 ore dopo l'iscrizione, fino a 5 esecuzioni al giorno e al massimo 2 crediti per esecuzione. Altri metodi di accesso iniziano senza crediti.
02
Examples
Real outputs from the public examples of this model on Replicate, with the prompt and settings that produced them. They were not generated live on this page.
Prompt
Close-up of a chameleon's eye, with its scaly skin changing color. Ultra high resolution 4k.
Durata: 0:05
Settings
num_frames
121
fps
24
Prompt
A pristine snowglobe featuring a winter scene sits peacefully. The globe violently explodes, sending glass, water, and glittering fake snow in all directions. The scene is captured with high-speed photography.
Durata: 0:05
Settings
num_frames
121
fps
24
Prompt
The video opens with a close-up of a woman in a white and purple outfit, holding a glowing purple butterfly. She has dark hair and walks gracefully through a traditional Japanese-style village at night
Durata: 0:05
Settings
num_frames
121
fps
24
03
Informazioni su Mochi 1
RiassuntoA partire da 23 settembre 2026
Mochi 1 è un modello di Community nella categoria Generazione video. Su Railwail, Mochi 1 costa ≈ 0,5041 USD per esecuzione. Proporzioni supportate: 16:9, 9:16 e 1:1.
Sfondo
Informazioni su Genmo
Fondato 2023 · San Francisco, USA
Genmo was founded in 2023 by Paras Jain (CEO) and Ajay Jain (CTO), both PhDs from UC Berkeley's BAIR lab, with a focus on open-source generative video. The company released the early Replay product and the smaller GEN-1 video model before launching Mochi 1 in October 2024 as a 10B-parameter open-weight text-to-video model under the Apache 2.0 licence -- at the time the largest open-source video model with a fully permissive licence. Genmo positions Mochi 1 as a research foundation for the community to fine-tune, extend and study, in deliberate contrast to closed competitors. The company has raised over $30M from investors including NEA and the Y Combinator network.
Asymmetric Diffusion Transformer (AsymmDiT) at 10B parameters with custom 3D VAE
Mochi 1 is a 10B-parameter Asymmetric Diffusion Transformer (AsymmDiT) operating on a high-compression 3D causal Variational Autoencoder. The 'asymmetric' design uses dramatically more parameters for the video stream than for the text stream while sharing self-attention, on the hypothesis that visual modeling is the bottleneck for video generation. Position information uses 3D RoPE; the model is trained with Rectified Flow Matching at full resolution and high motion intensity. Text conditioning uses a T5-XXL encoder. Mochi 1 generates 5-second clips at 480p / 30 fps natively (with a 720p HD variant in preview at launch). Training is done on a curated, filtered video corpus with dense captions produced by an in-house captioner. The team explicitly report ablations on resolution scheduling, motion intensity filtering and caption quality.
Parametri
10 billion
CapacitÃ
10B open-weight text-to-video model under Apache 2.0 (most permissive in class)
Asymmetric DiT design biased toward visual capacity
curl https://railwail.com/api/v1/videos/generations \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise"
}'
# Response: {"job_id": "...", "status": "queued", ...}
# Poll until status is completed, failed or cancelled:
curl https://railwail.com/api/v1/jobs/JOB_ID \
-H "Authorization: Bearer $RAILWAIL_API_KEY"
import os
import time
import requests
API = "https://railwail.com/api/v1"
headers = {"Authorization": f"Bearer {os.environ['RAILWAIL_API_KEY']}"}
job = requests.post(
f"{API}/videos/generations",
headers=headers,
json={
"model": "mochi-1-genmo",
"prompt": "A slow drone shot over a misty pine forest at sunrise",
},
).json()
while True:
status = requests.get(f"{API}/jobs/{job['job_id']}", headers=headers).json()
if status["status"] in ("completed", "failed", "cancelled"):
break
time.sleep(5)
print(status["status"], status.get("output_url"))
const API = "https://railwail.com/api/v1";
const headers = {
Authorization: `Bearer ${process.env.RAILWAIL_API_KEY}`,
"Content-Type": "application/json",
};
const job = await fetch(`${API}/videos/generations`, {
method: "POST",
headers,
body: JSON.stringify({
model: "mochi-1-genmo",
prompt: "A slow drone shot over a misty pine forest at sunrise"
}),
}).then((r) => r.json());
let status;
do {
await new Promise((r) => setTimeout(r, 5000));
status = await fetch(`${API}/jobs/${job.job_id}`, { headers }).then((r) => r.json());
} while (!["completed", "failed", "cancelled"].includes(status.status));
console.log(status.status, status.output_url);
Mochi 1 è un modello di Community nella categoria Generazione video. Su Railwail puoi richiamarlo con una chiave API tramite l'API Railwail.
Quanto costa Mochi 1 su Railwail?
Su Railwail, Mochi 1 costa ≈ 0,5041 USD per esecuzione. Ti viene addebitato ciò che ogni richiesta utilizza effettivamente. L'utilizzo viene pagato con crediti prepagati; 1 credito equivale a 0,01 USD.
Quali impostazioni supporta Mochi 1?
Secondo il suo schema di input, Mochi 1 conosce questi parametri: prompt (fino a 2000 caratteri), fps (8 a 30), seed, aspect_ratio (16:9, 9:16 o 1:1), duration_sec (1 a 6) e motion_strength (0 a 1).
Quanto è veloce Mochi 1?
Non ci sono ancora abbastanza esecuzioni misurate di Mochi 1 su Railwail per indicare un tempo di esecuzione. Dipende dall'input, dalle impostazioni e dal carico presso il provider.
Mochi 1 è migliore di Google Veo 3.1?
Dipende dall'attività . Mochi 1 (Community) e Google Veo 3.1 (Google DeepMind) sono entrambi modelli nella categoria Generazione video. La pagina di confronto mostra i loro prezzi e le specifiche affiancati.
Crea una chiave API Railwail e invia la tua richiesta con l'ID modello mochi-1-genmo. Gli esempi di codice per curl, Python e JavaScript sono nella sezione API di questa pagina.
Tencent's HunyuanVideo, a 13B open-weights text-to-video diffusion transformer. Produces high-motion, photorealistic clips with smooth temporal consistency and was one of the first open models to rival closed systems on motion quality.
